Biblio

The achievable bandwidth in ladder acoustic filters is strictly limited by the electromechanical coupling coefficient (k;) in conventional ladder-acoustic filters. Furthermore, their out-of-band rejection is inherently weak due to the frequency responses of the shunt or series-connected acoustic resonators. This work proposes a coupling-matrix-based solution for both issues by employing acoustic and electromagnetic resonators within the same filter prototype using prescribed Chebyshev responses. It has been shown that significantly much wider bandwidths, that cannot be achieved with acoustic-only filters, can be obtained. An important strength of the proposed method is that a filter with a particular FBW can be designed with a wide range of acoustic resonators with different k; values. An 14 % third-order asymmetrical-response filter is designed and fabricated using electromagnetic resonators and an acoustic resonator with a k; of 3.5 %.

Acoustic wave (AW) synthesis methodologies have become popular among AW filter designers because they provide a fast and precise seed to start with the design of AW devices. Nowadays, with the increasing complexity of carrier aggregation, there is a strong necessity to develop synthesis methods more focused on multiport filtering schemes. However, when dealing with multiport filtering functions, numerical accuracy plays an important role to succeed with the synthesis process since polynomial degrees are much higher as compared to the standalone filter case. In addition to polynomial degree, the number set of polynomial coefficients is also an important source of error during the extraction of the circuital elements of the filter. Nonetheless, in this paper is demonstrated that coupling matrix approaches are the best choice when the objective is to synthesize filtering functions with complex roots in their characteristic polynomials, which is the case of the channel polynomials of the multiport device.

In this study, a truncated singular value decomposition (TSVD) based computationally efficient through the wall imaging (TWI) is addressed. Mainly, two different scenarios with identical and non-identical multiple scatterers behind the wall have been considered. The scattered data are processed with special scheme in order to improve quality of the results and measurements are performed at four different frequencies. Next, effects of selecting truncation threshold in TSVD methods are analyzed and a detailed quantitative comparison is provided to demonstrate capabilities of these TSVD methods over selection of truncation threshold.

To solve the high-resolution three-dimensional (3D) microwave imaging is a challenging topic due to its inherent unmanageable computation. Recently, deep learning techniques that can fully explore the prior of meaningful pattern embodied in data have begun to show its intriguing merits in various areas of inverse problem. Motivated by this observation, we here present a deep-learning-inspired approach to the high-resolution 3D microwave imaging in the context of Generative Adversarial Network (GAN), termed as GANMI in this work. Simulation and experimental results have been provided to demonstrate that the proposed GANMI can remarkably outperform conventional methods in terms of both the image quality and computational time.